Our understanding of brain functions is hindered by the lack of detailed knowledge of synaptic connectivity in the underlying neural network. While synaptic connectivity of small neural circuits can be determined with electron microscopy, studies of connectivity on a larger scale, e.g. whole mouse brain, must be based on light microscopy imaging. It is now possible to fluorescently label subsets of neurons in vivo and image their axonal and dendritic arbors in 3D from multiple brain tissue sections. The overwhelming remaining challenge is neurite tracing, which must be done automatically due to the high-throughput nature of the problem. Currently, there are no automated tools that have the capacity to perform tracing tasks on the scale of mammalian neural circuits. Needless to say, the existence of such a tool is critical both for basic mapping of synaptic connectivity in normal brains, as well as for describing the changes in the nervous system which underlie neurological disorders. With this proposal we plan to continue the development of Neural Circuit Tracer - software for accurate, automated reconstruction of the structure and dynamics of neurites from 3D light microscopy stacks of images. Our goal is to revolutionize the existing functionalities of the software, making it possible to: (i) automatically reconstruct axonal and dendritic arbors of sparsely labeled populations of neurons from multiple stacks of images and (ii) automatically track and quantify changes in the structures of presynaptic boutons and dendritic spines imaged over time. We propose to utilize the latest machine learning and image processing techniques to develop multi-stack tracing, feature detection, and computer-guided trace editing capabilities of the software. All tools and datasets created as part of this proposal will be made available to the research community.